Data Engineering & Open Lakehouse Services
Vendor-Neutral Apache Iceberg Lakehouse on AWS for Data-Intensive Organizations
STX Next builds AWS-native, Apache Iceberg lakehouses for organizations requiring cost-efficient storage, multi-engine flexibility, and direct data control.
By storing data in S3 and managing tables via Iceberg, we enable diverse query engines – including Athena, Spark, Trino, and Redshift – to power your workloads. This open architecture delivers production-grade governance and operational discipline for high-volume, audit-heavy, and multi-domain analytics.
Our Approach to the Apache Iceberg Open Lakehouse
Apache Iceberg is an open table format for large analytic datasets on cloud object storage (S3, GCS, ADLS). It brings warehouse-grade capabilities – including ACID transactions, schema evolution, partition pruning, and time-travel – directly to open Parquet files on your own storage.
Serving as an engine-flexible lakehouse foundation, Iceberg allows diverse engines (such as Spark, Trino, Flink, and BigQuery) to access the same data. This eliminates the need to rewrite your underlying data format when changing compute engines, BI tools, or cloud providers.
ACID Transactions on Object Storage
Iceberg enforces snapshot isolation, preventing dirty reads and write conflicts even with multiple concurrent writers, a capability not native to plain data lakes.
Schema and Partition Evolution
Column types, partition strategies, and sort orders can be changed without rewriting existing data or causing downtime for downstream consumers.
Time-Travel and Audit
Every write creates an immutable snapshot. Teams can query data at any prior point in time, roll back bad writes, and produce audit trails without a separate archiving process.
Multi-Engine Query Access
Athena, Trino, Spark, and Redshift Spectrum can all query the same Iceberg tables, allowing teams to choose the right compute engine for each workload without replicating data.
Open Table Format
Data is stored as Parquet files with an Iceberg metadata layer on S3. Iceberg-compatible engines can access the same tables through a shared catalog, while governance is enforced through the surrounding platform layer - for example AWS Glue Data Catalog, Lake Formation, IAM, and engine-specific controls.
We treat Iceberg as an engineering-first lakehouse foundation, not a fully managed shortcut. Its benefits: openness, cost control, and multi-engine flexibility; demand deliberate catalog design, table maintenance, and governance tailored to actual access patterns, retention needs, and team maturity.
Iceberg provides more direct storage and engine control than Snowflake, and offers a more open, AWS-compatible foundation than Databricks. This makes it highly effective for cost-sensitive, high-volume, multi-engine environments that possess strong platform engineering discipline.
Apache Iceberg Lakehouse Architecture
A production-grade Iceberg environment on AWS relies on a multi-layered stack designed for openness, governance, and operational reliability. We structure engagements around these core components:
- Open Data Foundation: Data remains in open Parquet files managed through Apache Iceberg, reducing dependency on a single compute or warehouse platform.
- AWS-Native Economics: S3 provides cost-efficient storage for large historical and audit-heavy datasets, while Athena, Spark, Trino, or Redshift Spectrum can be selected based on workload needs.
- Multi-Engine Flexibility: Different teams can use different engines for engineering, BI, ad-hoc analysis, and batch processing without creating unnecessary data copies.
- Governance and Audit Support: Iceberg snapshots, table history, and controlled promotion patterns support rollback, traceability, and audit workflows when combined with Lake Formation, IAM, retention policies, and documented operating procedures.
- Pragmatic Scalability: The architecture is strongest when data volume, storage economics, or multi-engine access justify the additional operational responsibility.
- Operational Maintenance: Iceberg requires regular compaction, snapshot expiry, metadata cleanup, and orphan-file removal. We automate these routines and monitor table health to prevent small-file and metadata growth from silently degrading performance.
- Catalog Design: The catalog choice defines how tables are discovered, governed, and shared across engines. We select between AWS Glue Data Catalog, REST catalog, Polaris, Nessie, or lakeFS depending on AWS integration, multi-engine requirements, and governance model.
- Uneven Engine Support: Not every engine supports every Iceberg feature in the same way. We validate read/write patterns, row-level deletes, branching, views, and governance integration during the architecture phase rather than assuming feature parity.
- Governance Is Not Native to the Table Format Alone: Iceberg provides table metadata and transactional guarantees, but access control must be implemented through Lake Formation, IAM, catalog policies, and query-engine controls.
- Streaming Requires Careful Design: Iceberg is excellent for durable analytical storage, but ultra-low-latency use cases usually still require Kafka, Kinesis, Flink, or operational stores as the hot path.
Iceberg is not always the best default. For smaller analytics environments, teams without data platform engineering capacity, or organizations that need a highly managed SQL-first experience, Snowflake or Databricks may be a faster route to value.
=Iceberg becomes most attractive when data volume, storage economics, open-format requirements, and multi-engine access justify the additional operational responsibility.
How STX Next Adds Value with Apache Iceberg-Based Open Lakehouses
Our data engineering consulting practice helps organizations implement Apache Iceberg lakehouses shaped around actual data volumes, compliance requirements, and team structures. Whether the challenge is replacing an on-premise Hadoop cluster, eliminating costly proprietary warehouse storage, consolidating 50 or more source systems into a governed lake, or building real-time pipelines with a durable open-format archive, we bring the engineering depth to deliver it.
STX Next data lake consulting services help make data actionable by:
Why choose us?
Open Lakehouse Engineering
We design Iceberg platforms as production systems, not just table-format experiments.
AWS-Native Delivery
We combine S3, Glue, Lake Formation, Athena, EMR/Glue Spark, MWAA, dbt, and Terraform into maintainable delivery patterns.
Operational Discipline
We build compaction, snapshot expiry, orphan-file cleanup, monitoring, access control, and cost governance into the platform from the beginning.
Pragmatic Architecture
We recommend Iceberg only when openness, storage economics, or multi-engine access justify the extra operational responsibility.
Our Apache Iceberg Data Engineering Services
Lakehouse Architecture & Platform Design
Design starts with Iceberg catalog selection (AWS Glue Data Catalog, Polaris, or Nessie), S3 bucket and prefix layout for the Medallion architecture, Lake Formation permission model, and compute engine selection per workload type. For multi-region or multi-cloud requirements, we configure Iceberg REST catalogs that allow Athena, Spark, and Trino to share governance metadata without replication.
Data Migration & Warehouse Modernization
Migration from Hive-partitioned lakes, legacy on-premise warehouses, or proprietary managed warehouse formats includes table migration to Iceberg using snapshot-based or reserialization approaches, schema mapping, and row-level reconciliation. Iceberg's in-place migration tooling means existing Parquet files can be registered as Iceberg tables without rewriting data, reducing migration time and risk significantly.
Real-Time & Batch Ingestion Pipelines
We build ingestion pipelines matched to actual latency requirements: dltHub for incremental file and API ingestion with built-in state tracking and schema inference, AWS Glue for batch ETL workloads, and Kafka or Kinesis for event-stream ingestion into Iceberg via Flink or Spark Structured Streaming. Every pipeline includes deduplication logic, late-arrival handling, and data quality checks using Great Expectations or dbt tests before data reaches the Silver layer.
dbt Transformation & Data Quality
We build dbt models covering Bronze-to-Gold Medallion transformation logic and dimensional modeling for BI. Every model ships with schema tests, source freshness checks, and CI/CD deployment via GitHub Actions or AWS CodePipeline. For pipelines requiring transactional guarantees before promotion, we implement Iceberg's Write-Audit-Publish pattern so that data is validated in a staging branch before it is committed to the production table.
Governance, Compliance & Security Configuration
We configure the full governance stack: AWS Lake Formation row-, column-, and cell-level controls, LF-tags, IAM policies, Glue Data Catalog metadata, and query-engine-specific masking or filtering patterns. For financial services and healthcare clients, this includes architectures aligned with GDPR and HIPAA data governance requirements, with Iceberg snapshot history used as part of the auditability and rollback model.
ML & AI Pipeline Integration
We extend the open lakehouse into ML and AI by using Iceberg snapshots to create reproducible training datasets and AWS-native services such as SageMaker or Bedrock for model development, batch inference, and retrieval workflows. Where online serving, feature reuse, or low-latency vector search is required, we design the additional serving layer explicitly rather than hiding it inside the lakehouse narrative.
Expertise Built On 100+ Data Engineering Projects
Partnering with us, our clients have cut incident response times from days to minutes, consolidated thousands of redundant dashboards into focused reporting, and built systems that could never have run on their previous infrastructure.
AI-Powered Threat Management
A cybersecurity organization processing telemetry from more than 50 distributed systems consolidated operational and analytical workloads into a governed lakehouse platform designed and built by STX Next. The solution combined real-time enrichment pipelines, centralized governance, and scalable historical analytics, reducing incident investigation time from days to minutes while improving cross-team data access control.
Streamlining Insurance Data
We assisted a UK insurer in migrating millions of records from a legacy warehouse to a modern open lakehouse. With automated ingestion pipelines and dbt-based transformation, processing latency dropped sharply, enabling near-real-time data access for underwriting and claims teams.
Integrating Nonprofit Fundraising Data
STX Next built a scalable data exchange framework for a global open-source nonprofit, connecting donation, petition, and newsletter platforms. Automated reconciliation scripts and BigQuery pipelines eliminated reporting mismatches and improved campaign visibility across Salesforce and email tools.
Which Businesses Will Benefit Most from an Apache Iceberg Lakehouse?
Large Enterprises on AWS
Organizations storing petabytes of data across S3 who need ACID guarantees, time-travel, and multi-engine access without paying managed warehouse storage premiums.
Regulated Industries
Healthcare and financial services teams requiring fine-grained access control, retention policies, auditable change history, and deletion workflows that can be implemented using Iceberg row-level deletes where engine support and governance requirements allow.
Teams Replacing Proprietary Warehouses
Engineering leaders reducing dependence on vendor-controlled formats who want to keep data in open Parquet while retaining governance and query performance.
Multi-Engine Data Platforms
Organizations running Spark for engineering, Athena for analyst SQL, and Trino for federated queries who need a single governed data layer without replication pipelines between engines.
How we work
Discovery & Assessment
We map data sources, ingestion patterns, existing governance structures, and current storage costs. You receive a written assessment of current-state architecture and a prioritized implementation scope, including an estimated cost comparison between your existing setup and an Iceberg-based open lakehouse.
Architecture & Cost Modeling
We design the target lakehouse, Iceberg catalog configuration, Lake Formation permission model, and AWS deployment layout. You receive a cost model based on actual data volumes and a phased project plan.
Proof of Concept
We build a working Iceberg environment with a representative slice of data to validate query performance across engines, ingestion latency, compaction schedules, and cost projections before full commitment.
Full Implementation
Execution in two-week Scrum sprints covering full migration to Iceberg, ingestion pipelines, dbt models with CI/CD, Lake Formation governance setup, and BI connector integration.
Handoff & Optimization
We deliver runbooks, table maintenance playbooks, and onboarding sessions. Optional retained support includes monthly cost optimization, compaction tuning, and architecture guidance.
Let's talk
Schedule a chat with Head of Data Engineering and one of our senior engineers to discuss your Apache Iceberg needs.
FAQ
What does an Apache Iceberg implementation look like with STX Next?
We handle the full process from Iceberg catalog setup and S3 layout design through ingestion pipelines, dbt transformation, Lake Formation governance, and BI connectivity. The approach is tailored to each client's existing AWS environment, data volumes, and compliance requirements.
Why Apache Iceberg rather than a managed warehouse?
Managed warehouses store data in proprietary formats on vendor-controlled storage, creating lock-in and storage cost premiums. Apache Iceberg stores data in open Parquet files on your own S3 buckets, so you can query the same tables with Athena, Spark, Trino, or Redshift Spectrum without unnecessary replication, and add compatible engines without rewriting the underlying data format, provided catalog, governance, and feature support are validated.
Does STX Next support multi-cloud Apache Iceberg deployments?
Yes. While our primary AWS Iceberg stack uses Glue Data Catalog and Lake Formation, we configure Iceberg REST catalogs (Polaris, Nessie) for organizations that need the same tables accessible from Azure or GCP compute engines alongside AWS services.
Which industries benefit most from open lakehouse data engineering services?
Organizations with high data volumes, strict governance requirements, or existing multi-engine environments benefit most. STX Next has delivered data engineering engagements across financial services, insurance, cybersecurity, healthcare, and e-commerce. Regulated sectors particularly benefit from Iceberg’s snapshot history, rollback capabilities, and support for deletion workflows, when combined with appropriate retention, access-control, and downstream data management processes.
How does an Apache Iceberg lakehouse reduce data infrastructure costs?
Data sits in S3 at standard object storage rates, avoiding the storage markup of managed warehouses. Iceberg's compaction reduces small-file overhead over time, keeping query costs low. STX Next further optimizes spend by right-sizing AWS Glue job configurations, automating snapshot expiry, and consolidating redundant pipelines that currently move data between systems.
Can STX Next migrate our existing data lake or warehouse to Apache Iceberg?
Yes. We use Iceberg's in-place migration tooling to register existing Parquet-based Hive partitioned tables as Iceberg tables without rewriting data where possible. For proprietary warehouse formats, we plan a phased migration that keeps existing workloads running during the transition.
